System and method for reducing noise from an image

ABSTRACT

Systems and methods for reducing noise in an image are provided. Noise is reduced in a luminance channel of the image using a first filtering procedure. Noise is reduced in a chrominance channel of the image using a second filtering procedure. The chrominance channel is decomposed into a plurality of frequency sub-bands, where each frequency sub-band of the plurality of frequency sub-bands represents the chrominance channel at a first resolution. The noise is further reduced in the chrominance channel using a third filtering procedure. The third filtering procedure is applied to each frequency sub-band of the plurality of frequency sub-bands. A lowest frequency sub-band of the plurality of frequency sub-bands is decomposed into a second plurality of frequency sub-bands after the third filtering procedure. Each frequency sub-band of the second plurality of frequency sub-bands represents the chrominance channel at a second resolution that is lower than the first resolution.

CROSS-REFERENCE TO RELATED APPLICATIONS

This disclosure claims priority to U.S. Provisional Patent ApplicationNo. 61/860,849, filed on Jul. 31, 2013, which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

The technology described in this document relates generally to the fieldof image processing and more particularly to systems and methods forreducing noise from an image.

BACKGROUND

High resolution complementary metal-oxide-semiconductor (CMOS) andcharge-coupled device (CCD) image sensors are highly demanded in today'smarket for their use in acquiring high quality images. For example, suchimage sensors may be used in digital cameras, smart phone cameras, andother digital devices including camera functionality. However, in orderto keep die sizes small, pixel sizes used in these image sensors may bereduced. The smaller pixel size lowers the image sensor's ability todetect a number of impinging photons, thus causing images acquired bythe image sensor to have a lower signal-to-noise ratio (SNR). The SNRdecreases further when the image sensor is being used in a low lightcondition.

SUMMARY

The present disclosure is directed to systems and methods for reducingnoise in an image. In an example method of reducing noise in an image,noise is reduced in a luminance channel of the image using a firstfiltering procedure. Noise is reduced in a chrominance channel of theimage using a second filtering procedure. The chrominance channel isdecomposed into a first plurality of frequency sub-bands after thesecond filtering procedure, where each frequency sub-band of the firstplurality of frequency sub-bands represents the chrominance channel at afirst resolution. The noise is further reduced in the chrominancechannel using a third filtering procedure after the decomposing of thechrominance channel into the first plurality of frequency sub-bands. Thethird filtering procedure is applied to each frequency sub-band of thefirst plurality of frequency sub-bands. A lowest frequency sub-band ofthe first plurality of frequency sub-bands is decomposed into a secondplurality of frequency sub-bands after the third filtering procedure.Each frequency sub-band of the second plurality of frequency sub-bandsrepresents the chrominance channel at a second resolution that is lowerthan the first resolution.

In another example, an example system for reducing noise in an imageincludes a first filter configured to reduce noise in a luminancechannel of the image using a first filtering procedure. A second filteris configured to reduce noise in a chrominance channel of the imageusing a second filtering procedure. The example system further includesa first transformation unit configured to decompose the chrominancechannel into a first plurality of frequency sub-bands after the secondfiltering procedure. Each frequency sub-band of the first plurality offrequency sub-bands represents the chrominance channel at a firstresolution. A third filter is configured to further reduce the noise inthe chrominance channel using a third filtering procedure after thedecomposing of the chrominance channel into the first plurality offrequency sub-bands. The third filtering procedure is applied to eachfrequency sub-band of the first plurality of frequency sub-bands. Theexample system also includes a second transformation unit configured todecompose a lowest frequency sub-band of the first plurality offrequency sub-bands into a second plurality of frequency sub-bands afterthe third filtering procedure. Each frequency sub-band of the secondplurality of frequency sub-bands represents the chrominance channel at asecond resolution that is lower than the first resolution.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A is a block diagram depicting an example system for reducingnoise in an image.

FIG. 1B is a block diagram depicting an example of an independentprocessing of luminance and chrominance channels using two differentnoise reduction methods.

FIG. 2 is a block diagram depicting an example multi-resolution filterincluding bilateral filtering procedures and wavelet threshold filteringprocedures.

FIG. 3 is a diagram illustrating an example wavelet transformationimage.

FIG. 4 is a block diagram illustrating an example system for reducingnoise in an image that includes a noise analyzer and a noise reductionmodule.

FIG. 5 is a block diagram for processing a YUV image using first andsecond multi-resolution filters.

FIG. 6 is a block diagram for processing a YUV image including ablock-matching three-dimensional filter and a multi-resolution filter.

FIG. 7 is a flowchart illustrating an example method for reducing noisein an image.

DETAILED DESCRIPTION

FIG. 1A is a block diagram 100 depicting an example system for reducingnoise in an image. In the example of FIG. 1A, a color input image isreceived in a Red Green Blue (RGB) format 102 and thereafter convertedto a second, different format. In the example of FIG. 1A, the secondformat is a YUV format 104, but in other examples, additional othersecond formats are used (e.g., a perceptually uniform CIE-L*A*B* colorspace format, etc.). In an example, the conversion from the RGB format102 to the YUV format 104 is performed in order to allow constituentcomponents of the color input image to be subject to different types ofprocessing. For example, when image data processing is performed in theRGB format 102, the three components (i.e., red, green, and bluecomponents) are necessarily subject to the same processing. By contrast,in the case of the YUV format 104, a luminance Y channel can beprocessed separately from a color-difference U channel and acolor-difference V channel. The luminance Y channel of the YUV format104 (also known as the “luma” component of the YUV image) indicates abrightness of the image. The color-difference U and color-difference Vchannels of the YUV format 104 (also known as “chroma” components of theYUV image) indicate a color of the image.

In the example system of FIG. 1A and in additional examples describedbelow, a noise reduction framework utilizes separate, differentprocessing methods for filtering the luminance and chrominance channels106, 108 of the input image. Specifically, in processing the luminancechannel 106, a first method is used that places an emphasis onpreserving edges and textures in the luminance channel 106. Inprocessing the chrominance channel 108, a second, different method isused that places an emphasis on removal of both coarse grain noise andfine grain noise from the chrominance channel 108. In an example, theprocessing of the luminance and chrominance channels 106, 108 occurs viatwo separate filters, where the two separate filters process theluminance and chrominance channels 106, 108 independently of each otherusing different filter parameters. The use of the separate, differentprocessing methods for reducing noise in the luminance and chrominancechannels 106, 108 is in contrast to alternative noise reduction methodsthat use same or similar procedures in reducing noise from both of thechannels 106, 108.

An example of the independent processing of the luminance andchrominance channels 106, 108 is illustrated in FIG. 1A. In FIG. 1A, theluminance channel (Y) 106 of the image is processed via edge preservingdenoising methods 109 to generate a filtered luminance channel (Y_(f))112. The processing of the luminance channel 106 via the edge preservingdenoising methods 109 reflects the fact that the Y channel of an imagegenerally includes more edge information and texture information ascompared to the U and V channels of the image. In an example, in orderto preserve the edge information and texture information in theluminance channel 106, the edge preserving denoising methods 109 includea block-matching three-dimensional (BM3D) filtering procedure. The BM3Dfiltering procedure removes noise from the luminance channel 106 withoutrequiring a decomposition of the luminance channel 106 into frequencysub-bands. In another example, the edge preserving denoising methods 109include a multi-resolution filtering (MRF) procedure that utilizes sucha decomposition of the luminance channel 106 into frequency sub-bands.When the MRF procedure is used to filter the luminance channel 106,parameter settings that place an emphasis on maintaining edges andtextures in the luminance channel 106 are used. The BM3D and MRFfiltering procedures are described in further detail below.

In FIG. 1A, the chrominance channel (UV) 108 of the image is processedusing denoising methods 110 to generate a filtered chrominance channel(U_(f)V_(f)) 114. As illustrated in FIG. 1A, the denoising methods 110specifically include coarse grain noise removal and fine grain noiseremoval procedures. The processing of the chrominance channel 108 usingsuch coarse and fine grain noise removal procedures reflects the factthat the U and V channels of the input image generally includesignificant chrominance noise (i.e., color noise) including both coarseand fine grain noise. In an example, the chrominance noise includes lowfrequency noise having a relatively large noise size (e.g., a noise sizeincluding 10-20 pixel patches of the image or larger). Such lowfrequency noise differs from high frequency noise (also known as “saltand pepper” noise) that includes a relatively small noise size that ison the order of a single pixel or several pixels. High frequency noisecan be removed via conventional methods that involve defining a window(e.g., a kernel) of pixels that is larger than the size of the noise andthen performing noise removal techniques (e.g., smoothing techniques)within the window to remove the noise within the window.

Such conventional methods are generally unable to remove low frequencynoise, however. For example, in attempting to remove the low frequencynoise via the conventional methods, a required window size may beunreasonably large to accommodate the relatively large size of the lowfrequency noise, and it may be difficult to distinguish the lowfrequency noise from actual features of the image, etc. As described infurther detail below, the coarse and fine grain noise removal methods110 present alternative techniques for the removal of the relativelylarge noise included in the chrominance channel 108.

Specifically, the coarse and fine grain removal methods 110 utilizeimage decomposition procedures (e.g., discrete wavelet transformations)to decrease the size of the chrominance noise features, thus enablingthe chrominance noise features to be removed via conventionaltechniques, such as the window-based smoothing techniques describedabove. In an example, an image decomposition procedure is used todownsample and reduce the size of the chrominance channel 108 of theimage. In the example, the chrominance channel 108 is downsampled by afactor of two and reduced in size by a factor of four, such that thechrominance channel 108, following such procedures, has a resolution ofone-half the original resolution and a size of one-fourth of theoriginal size. After the first decomposition, the downsampled andsize-reduced features of the chrominance channel 108 are subject tonoise filtering. After the noise filtering, a second decompositionprocedure including additional downsampling and size reduction isperformed on the chrominance channel 108. With each subsequentdecomposition, a size of the chrominance noise decreases, thus enablingthe filtering of the chrominance noise using the conventional techniquesdescribed above.

As an example of the use of the decomposition procedures, chrominancenoise with dimensions on the order of 32 pixels is reduced in size to beon the order of 8 pixels using a first decomposition, and a seconddecomposition reduces the chrominance noise size to be on the order of 2pixels. After performing this size reduction, the chrominance noise withthe size on the order of 2 pixels can be filtered via a window-basedsmoothing technique with a window size of 7 pixels×7 pixels, forexample.

In addition to the decomposition procedures described above, the coarseand fine grain noise removal methods 110 also utilize filteringprocedures prior to each decomposition. In this manner, chrominancenoise is removed at each step of the decomposition and not only afterthe chrominance channel 108 has been decomposed to a lowest size andresolution. In an example, in order to remove such coarse and fine grainnoise in the chrominance channel 108, the coarse and fine grain noiseremoval methods 110 include an MRF filtering procedure that involves oneor more decompositions of the chrominance channel 108. The MRF filteringprocedure utilizes second parameter settings focusing on noise removal,and such second parameter settings differ from the first parametersettings discussed above for the use of the MRF filter in the edgepreserving denoising methods 109.

FIG. 1B is a block diagram 150 depicting an example of the different,independent processing that is used in reducing noise from the luminanceand chrominance channels 106, 108. As depicted in FIG. 1B, noise isreduced in the luminance channel 106 via a first filtering procedure109A. In an example, the first filtering procedure 109A utilizes a BM3Dfilter that does not decompose the luminance channel 106. In anotherexample, the first filtering procedure 109A utilizes an MRF filteringprocedure that decomposes the luminance channel 106 in a manner similarto the decomposition procedures discussed above.

In processing the chrominance channel 108 using the coarse and finegrain noise removal methods 110 to generate the filtered chrominancechannel 114, multiple filtering procedures are used, where the multiplefiltering procedures are used during a decomposition of the chrominancechannel 108. Thus, as illustrated in FIG. 1B, noise is reduced in thechrominance channel 108 using a second filtering procedure 110A. In anexample, the second filtering procedure 110A uses a bilateral filteringtechnique that is described in further detail below. At 110B, thechrominance channel 108 is subject to a decomposition procedure (e.g., adecomposition procedure similar to those described above) after thesecond filtering procedure 110A.

Noise is further reduced in the chrominance channel 108 using a thirdfiltering procedure 110C after the decomposing of the chrominancechannel 108 at 110B. In an example, the decomposing of the chrominancechannel 108 at 110B causes a plurality of frequency sub-bands (e.g., LL,LH, HL, and HH frequency sub-bands) to be generated. The third filteringprocedure 110C is applied to each frequency sub-band of the plurality offrequency sub-bands. In an example, the third filtering procedure 110Cuses i) a bilateral filtering procedure on one or more of the frequencysub-bands of the plurality of frequency sub-bands, and ii) a waveletthresholding filtering procedure on other frequency sub-bands of theplurality of frequency sub-bands. Specifically, in the example, thebilateral filtering procedure is used on an LL frequency sub-band of theplurality of frequency sub-bands, and the wavelet thresholding filteringprocedure is used on LH, HL, and HH frequency sub-bands of the pluralityof frequency sub-bands. It should be understood that the noise removalvia the second filtering procedure 110A and the third filteringprocedure 110C is cumulative. Thus, at the start of the third filteringprocedure 110C, a noise level of the chrominance channel 108 has alreadybeen reduced via the second filtering procedure 110A, and the thirdfiltering procedure 110C is then used to remove additional noise fromthe chrominance channel 108.

At 110D, a lowest frequency sub-band of the plurality of frequencysub-bands is decomposed into a second plurality of frequency sub-bandsafter the third filtering procedure 110C. In examples where a discretewavelet transformation is used to decompose the chrominance channel 108,the lowest frequency sub-band that is decomposed via the seconddecomposition 110D is an LL frequency sub-band. In an example,additional filtering procedures are performed on the second plurality offrequency sub-bands as part of the coarse and fine grain noise removalmethods 110. For example, as illustrated in FIG. 1B, a fourth filteringprocedure 110E may be used after the decomposing at 110D. The fourthfiltering procedure 110E is applied to each frequency sub-band of thesecond plurality of frequency sub-bands. In an example, the fourthfiltering procedure 110E uses i) a bilateral filtering procedure on oneor more of the frequency sub-bands of the second plurality of frequencysub-bands, and ii) a wavelet thresholding filtering procedure on otherfrequency sub-bands of the second plurality of frequency sub-bands.Specifically, in the example, the bilateral filtering procedure is usedon an LL frequency sub-band of the second plurality of frequencysub-bands, and the wavelet thresholding filtering procedure is used onLH, HL, and HH frequency sub-bands of the second plurality of frequencysub-bands.

As illustrated in the example of FIG. 1B, the example noise reductionsystem described herein utilizes filtering operations (e.g., second,third, and fourth filtering procedures 110A, 110C, and 110E) within adecomposition path. Utilizing the filtering operations in thedecomposition path thus causes there to be a filtering operation priorto each decomposition and not only after the chrominance channel 108 hasbeen decomposed to a lowest resolution.

FIG. 2 is a block diagram 200 depicting an example multi-resolutionfilter (MRF) including bilateral filtering procedures 204, 220 andwavelet threshold filtering procedures 222, 224, 226. As describedbelow, the MRF filter i) performs decomposition of an input image 202,and ii) reduces the noise in the image 202 at each stage of thedecomposition via the aforementioned bilateral filtering and waveletthresholding procedures. In the filtering performed by the example MRFfilter of FIG. 2, the input image 202 is in a YUV format including aluminance (Y) channel and a chrominance (UV) channel. Although thecolor-difference U channel and the color-difference V channel areprocessed together in the example of FIG. 2 and referred to collectivelyas the “chrominance channel.” it should be understood that thecolor-difference U and color-difference V channels are separated andprocessed independently in other examples.

The input image 202 is subject to a bilateral filtering procedure 204 togenerate a filtered image 206. The bilateral filtering procedure 204 isconfigured to perform spatial averaging over a window of pixels of theinput image 202 to reduce noise in the input image 202. Specifically,the bilateral filtering procedure operation 204 takes a weighted sum ofpixels in a local neighborhood of a particular pixel of the input image202 to generate a new value for the particular pixel. These aspects ofthe bilateral filtering procedure 204 are known to those of ordinaryskill in the art. In an example, the weights of the bilateral filteringprocedure 204 depend on both a spatial distance and an intensitydistance within the input image 202. At a particular pixel x, an outputof the bilateral filter is calculated according to a first equation:

$\begin{matrix}{{{\overset{\sim}{I}(x)} = {\frac{1}{C}{\sum\limits_{y \in {N{(x)}}}\;{{\mathbb{e}}^{\frac{- {{y - x}}^{2}}{2\;\sigma_{d}^{2}}}{\mathbb{e}}^{\frac{- {{{I{(y)}} - {I{(x)}}}}^{2}}{2\;\sigma_{r}^{2}}}{I(y)}}}}},} & \left( {{Eqn}.\mspace{14mu} 1} \right)\end{matrix}$where σ_(d) (i.e., “distance sigma”) and σ_(r) (i.e., “range sigma”) areparameters controlling a distribution of the weights in the spatial andintensity domains, respectively, N(x) is a spatial neighborhood of pixelx, I(x) is an intensity of the pixel x, I(y) is an intensity of a pixely that is in the spatial neighborhood, and C is a normalization constantdefined by a second equation:

$\begin{matrix}{c = {\sum\limits_{y \in {N{(x)}}}\;{{\mathbb{e}}^{\frac{- {{y - x}}^{2}}{2\;\sigma_{d}^{2}}}{{\mathbb{e}}^{\frac{- {{{I{(y)}} - {I{(x)}}}}^{2}}{2\;\sigma_{r}^{2}}}.}}}} & \left( {{Eqn}.\mspace{14mu} 2} \right)\end{matrix}$The distance sigma and range sigma values correspond, generally, to astrength of the bilateral filter. Thus, if large distance sigma andrange sigma values are used, a high filter strength is applied to theinput image 202, which may result in significant noise reduction (e.g.,smoothing) while causing loss of edge and texture information in theinput image 202. By contrast, if small distance sigma and range sigmavalues are used, a low filter strength is applied, which may preserveedges in the input image 202 but not adequately filter noise included inthe input image 202.

The filtered image 206 is subject to a discrete wavelet transformation(“DWT”) procedure 208 to decompose the filtered image 206. Indecomposing the filtered image 206, a plurality of frequency sub-bands210 are generated. As illustrated in FIG. 2, the plurality of frequencysub-bands 210 include the sub-bands LL(1), HL(1), LH(1), and HH(1). Thedecomposing of the filtered image 206 is accomplished, in an example,using at least two filters. In the example, the two filters comprise ahorizontal filter and a vertical filter. For example, for atwo-dimensional wavelet transformation, the decomposition operation isperformed on the filtered image 206 in a horizontal direction and avertical direction. The horizontal and vertical filters are high passfilters, low pass filters, or other filters.

FIG. 3 depicts aspects of the discrete wavelet transformation procedure208 and illustrates an example wavelet transformation image 300. In theexample shown in FIG. 3, an input image, which is the filtered inputimage 206 in the example of FIG. 2, is divided into HH(1), HL(1), LH(1),and LL(1) frequency sub-bands via the wavelet transformation procedure.“H” represents a high frequency component, and “L” represents a lowfrequency component. The HH sub-band is obtained by passing a horizontalcomponent and a vertical component of the image through a high frequencyfilter. The HL sub-band and the LH sub-band are obtained by passingeither the horizontal or the vertical component through a high frequencyfilter and passing the other component through a low frequency filter.The LL sub-band is obtained by passing the horizontal component and thevertical component through a low frequency filter. The HH sub-band is ahighest frequency sub-band of the four sub-bands, and the LL sub-band isa lowest frequency sub-band of the four sub-bands. In an example, the LLsub-band includes more chrominance noise (i.e., color noise) than theLH, HL, and HH sub-bands.

Wavelet transformation is used to decompose the input image multipletimes in the example of FIG. 3. The number shown in brackets refers to adecomposition level or a resolution level, such that in the example ofFIG. 3, the image has been decomposed three times, thus yielding threeresolution levels. Level 1 is a highest resolution level in FIG. 3, andLevel 3 is a lowest resolution level. It should be understood that thedecomposition procedure of FIG. 3 includes aspects of the decompositionprocedure described above with reference to FIG. 1A. For example, ingenerating the LL(1), HL(1), LH(1), and HH(1) frequency sub-bands, theinput image is downsampled to reduce the resolution of the input image,thus yielding the frequency sub-bands at the Level 1 resolution that islower than that of the original image. Further, a size of the inputimage is reduced by a factor of four in the LL(1), HL(1), LH(1), andHH(1) frequency sub-bands, as illustrated in FIG. 3.

With reference again to FIG. 2, following the decomposition performedvia the discrete wavelet transformation procedure 208 to generate theplurality of frequency sub-bands 210, each frequency sub-band of theplurality of frequency sub-bands 210 is subject to further filtering.Thus, as illustrated in FIG. 2, an LL(1) sub-band 212 is subject to asecond bilateral filtering procedure 220 to generate a filtered LL(1)sub-band 228. The second bilateral filtering procedure 220 is the sameor similar to the first bilateral filtering procedure 204 describedabove.

In FIG. 2, HL(1) 214, LH(1) 216, and HH(1) 218 frequency sub-bands aresubject to wavelet threshold filtering procedures 222, 224, 226,respectively. The wavelet threshold filtering procedures 222, 224, 226are used to separate noise signals from image signals in the HL(1) 214,LH(1) 216, and HH(1) 218 sub-bands. The discrete wavelet transformation208 transforms the input image 202 into the wavelet domain, such thateach of the frequency sub-bands 210 includes wavelet coefficients thatcorrespond to pixels. In the wavelet domain, image features arerepresented by large coefficients while noise features are representedby small coefficients. Thus, the wavelet threshold filtering procedures222, 224, 226 remove noise from the sub-bands 214, 216, 218 byeliminating coefficients that are less than a threshold value. Followingthe removal of the coefficients that are less than the threshold value,the remaining coefficients are used to reconstruct the image signals ofthe sub-bands 214, 216, 218. The filtering of the HL(1) 214, LH(1) 216,and HH(1) 218 sub-bands using the wavelet threshold filtering procedures222, 224, 226 results in the filtered sub-bands HL(1)′ 230, LH(1)′ 232,and HH(1)′ 234, respectively. In an example, the wavelet thresholdfiltering procedures 222, 224, 226 utilize a Bayes Shrink waveletthresholding algorithm that is known to those of ordinary skill in theart.

Additional discrete wavelet transformation procedures similar to thewavelet transformation procedure 208 are performed to decompose theLL(1) sub-band 212, where the LL(1) sub-band 212 is a lowest frequencysub-band of the plurality of frequency sub-bands 210. For example, FIG.2 illustrates a second discrete wavelet transformation 236 that isperformed on the filtered LL(1)′ 228 sub-band to generate additionalsub-bands LL(2), HL(2), LH(2), and HH(2). The LL(2), HL(2), LH(2), andHH(2) sub-bands are depicted in FIG. 3. At each level of decomposition,the LL sub-band is processed by a bilateral filter, and the HL, LH, andHH sub-bands are processed by wavelet thresholding filters. Additionaldiscrete wavelet transformation procedures may be performed to furtherdecompose the input image 202. For example, FIG. 3 depicts LL(3), HL(3),LH(3), and HH(3) sub-bands, and these sub-bands are generated byperforming a decomposition procedure on the LL(2) sub-band. Inaccordance with the example system described herein, the LL(2) sub-bandis subject to a filtering procedure utilizing a bilateral filter priorto the decomposition procedure.

The example MRF filter of FIG. 2 illustrates the use of the MRF filterto decompose the input image 202 and filter noise from the input image202 at each level of the decomposition process. For example, asdescribed above, the MRF filter decomposes the input image 202 intovarious resolution levels, and at each resolution level, the LL sub-bandis processed by a bilateral filter. Following the processing of the LLsub-band by the bilateral filter, the filtered LL sub-band is furtherdecomposed to generate frequency sub-bands of a lower resolution level.In this manner, the MRF filter of FIG. 2 uses the bilateral filter inthe decomposition (forward) path of the MRF filter rather than thereconstruction (reverse) path of the MRF filter. The use of thebilateral filter in the decomposition path is in contrast to alternativemethods that use the bilateral filter in the reconstruction of the imagefrom the frequency sub-bands.

FIG. 4 is a block diagram 400 illustrating an example system forreducing noise in an image 402 that includes a noise analyzer 404 and anoise reduction module 406. In FIG. 4, the noise reduction module 406includes a first filter 408 for reducing noise from a luminance channelof the image 402 and a second filter 410 for reducing noise from achrominance channel of the image 402. Thus, the noise reduction module406 receives the image 402, reduces the noise in the image 402 using thefirst and second filters 408, 410, and outputs a noise-corrected image414. In accordance with the examples described herein, the first andsecond filters 408, 410 of the noise reduction module 406 utilizeindependent, different processing methods in filtering noise from theluminance and chrominance channels. In an example, the first filter 408is a BM3D filter that does not require a decomposition of the image 402,and the second filter 410 is an MRF filter (e.g., an MRF filter similarto that described above in FIG. 2) that decomposes the image one or moretimes, as described above. In another example, the first filter 408utilizes a first MRF filtering procedure with first parameter settingsthat focus on preserving edges and textures in the luminance channel,and the second filter 410 utilizes a second MRF filtering procedure withsecond parameter settings that focus on removing noise from the image402.

In the example utilizing the first and second MRF filtering proceduresin the first and second filters 408, 410, respectively, both theluminance channel and the chrominance channel of the input image 402 aresubject to decomposition procedures. However, parameters of thedecomposition procedures vary based on the different characteristics ofthe luminance and chrominance channels. As described above, thechrominance channel generally includes more chrominance noise ascompared to the luminance channel. Due to the challenges posed by theremoval of the chrominance noise (e.g., challenges caused by therelatively large sizes of the chrominance noise features and otherchallenges described above with reference to FIG. 1A), the noisefiltering for the chrominance channel may require more levels ofdecomposition and filtering than the noise filtering for the luminancechannel.

In an example, the first MRF filtering procedure utilized in filteringthe luminance channel uses a single decomposition procedure. Thus, theluminance channel of the image 402 is decomposed into LL(1), HL(1),LH(1), and HH(1) sub-bands, and these sub-bands are filtered, but nofurther decomposition is performed on the luminance channel. Bycontrast, in the example, the second MRF filtering procedure utilized infiltering the chrominance channel uses two or three decompositionlevels. Thus, the chrominance channel of the image 402 is decomposedinto LL(1), HL(1), LH(1), and HH(1) sub-bands, and the LL(11) sub-bandis then further decomposed into LL(2), HL(2), LH(2), and HH(2)sub-bands. Additional decompositions may be utilized in processing thechrominance channel.

In an example, the BM3D or MRF filters employed in the first and secondfilters 408, 410 utilize filter parameters that are based on noisecharacteristics 412 of the image 402. In FIG. 4, a noise analyzer 404receives the image 402 and analyzes the image 402 to determine the noisecharacteristics 412. The noise analyzer 404 provides the noisecharacteristics 412 to the noise reduction module 406, and parametersettings of the first and second filters 408, 410 are determined basedon the noise characteristics 412.

In an example, the noise analyzer 404 determines standard deviation ofnoise values for the image 402. Because the image 402 is subject todecomposition at least in the second filter 410, the noise analyzer 404is configured to determine the standard deviation of noise values ateach decomposition level. Thus for each resolution level into which theimage 402 is decomposed, the noise analyzer 404 determines the standarddeviation of noise values. The standard deviation of noise valuescomprise at least a portion of the noise characteristics 412 that areprovided to the noise reduction module 406. Specifically, in an example,the noise analyzer 404 determines the standard deviation of noise valuesfor each unit of the image 402, where a unit of the image 402 comprisesa single pixel of the image 402 or a group (e.g., a block) of pixels ofthe image 402. Parameter settings of the first and second filters 408,410 are determined based on the standard deviation of noise values, asdescribed in further detail below with reference to FIGS. 5 and 6.

FIG. 5 is a block diagram 500 for processing a YUV image 502 using firstand second multi-resolution (MRF) filters 504, 510. The MRF filters 504,510 perform decomposition and noise-filtering on luminance (Y) andchrominance (UV) channels 503, 508, respectively, of the YUV image 502.The MRF filters 504, 510 are the same or similar to the examplemulti-resolution filter of FIG. 2 but may utilize more levels ofdecomposition or fewer levels of decomposition than are used in theexample of FIG. 2. The MRF filters 504, 510 are configured to performthe decomposition and noise-filtering on the luminance channel 503 andthe chrominance channel 508 independently and using different filterparameters. Based on the independent processing of the luminance channel503 and the chrominance channel 508, the MRF filters 504, 510 output afiltered Y_(f) component 506 and filtered U_(f)V_(f) component 512,respectively.

In accordance with the example described above with reference to FIG. 4,the MRF filters 504, 510 utilize filter parameters that are based onnoise characteristics of the YUV image 502. In an example, the noisecharacteristics of the YUV image 502 include standard deviation of noisevalues for the YUV image 502. The MRF filters 504, 510 utilize bilateralfilters and wavelet thresholding filters. As illustrated in Equation 1above, a bilateral filter includes three basic parameter settings: σ_(d)(i.e., “distance sigma”), σ_(r) (i.e., “range sigma”), and N(x) (i.e.,“window size”). A wavelet thresholding filter includes parametersettings that include a wavelet threshold value for waveletcoefficients. In an example, the window size and distance sigma are setto values of 11 pixels×11 pixels and 1.8, respectively, in the bilateralfilters utilized by the MRF filters 504, 510. The remaining variablesinclude the range sigma variable and the wavelet threshold value, whichare both dependent on the noise standard deviation σ_(n) for the YUVimage 502. Thus, in order to determine values for the range sigmavariables and wavelet threshold values, the noise standard deviationσ_(n) for the YUV image 502 is determined.

The noise standard deviation σ_(n) is determined via a noise analyzercomponent (e.g., the noise analyzer 404 of FIG. 4) or via another means.In an example, the determination of the noise standard deviation an isperformed on a pixel-by-pixel basis, such that a noise standarddeviation value is determined for each pixel of the YUV image 502. Thepixel-by-pixel determination of σ_(n) reflects the fact that noise islocation dependent and thus varies across the different pixels of theYUV image 502. In the pixel-by-pixel determination, σ_(n) is computedfor each pixel of the YUV image 502 based on the assumption that a smallwindow size at the center of the pixel follows a generalized Gaussiandistribution (“GGD”). Because the YUV image 502 is decomposed intomultiple different resolution levels by the MRF filters 504, 510, σ_(n)is determined at each resolution level of the multiple differentresolution levels. Specifically, the noise standard deviation σ_(n) ateach of the multiple different resolution levels is calculated from theHH_(t) frequency sub-band, where t indicates the resolution level ordecomposition level. The calculation of the noise standard deviationσ_(n) at each of the multiple different resolution levels is in contrastto alternative noise reduction methods that utilize only the HH₁frequency sub-band in estimating the noise standard deviation σ_(n). Insuch alternative noise reduction methods, the utilization of only theHH₁ frequency sub-band causes the noise standard deviation σ_(n) to bedetermined at a single resolution level with t=1.

In determining the noise standard deviation σ_(n) at each resolutionlevel, a modified robust median estimation method is used in an example.The modified robust median estimation method is defined based on anequation

$\begin{matrix}{{\left( \sigma_{n} \right)_{t,{ij}} = \frac{{Median}\left( {Y_{t,{ij}}} \right)}{0.6745}},} & \left( {{Eqn}.\mspace{14mu} 3} \right)\end{matrix}$where (σ_(n))_(t,ij) is a standard deviation of noise for the YUV image502 at a resolution level of t for a pixel of the YUV image 502 havingcoordinates of (i, j), and Y_(t,ij)ε a window size of 11×11 at thecenter of (i,j) at level t.

In determining the range sigma σ_(r) parameter values for the MRFfilters 504, 510, a scaled version of the standard deviation of noiseσ_(n) is used in an example. Specifically, in determining the rangesigma value at each of the resolution levels (σ_(r))_(t), a scaledversion of the standard deviation of noise (σ_(n))_(t) is used. Becausethe noise filtering framework of FIG. 5 utilizes separate, differentprocessing methods for filtering the luminance and chrominance channels503, 508 of the YUV image 502, the parameter settings for the first MRFfilter 504 differ from those of the second MRF filter 510. For example,the luminance channel 503 generally includes significant texture andedge information. Thus, in order to preserve as much texture and edgeinformation as possible in the luminance channel 503, each pixel fromthe LL_(t) frequency sub-band has its own range sigma σ_(r) based on(σ_(n))_(t) in the first MRF filter 504. In an example, the range sigmaσ_(r) for filtering the luminance channel 503 in the first MRF filter504 is determined according to:

$\begin{matrix}{{\left( \sigma_{r} \right)_{t,{ij}}^{Y} = {m_{2} \times \left( \sigma_{n} \right)_{t,{ij}}}},} & \left( {{Eqn}.\mspace{14mu} 4} \right)\end{matrix}$where (σ_(n))_(t, ij) is a standard deviation of noise value for the YUVimage 502 at a resolution level of t for a pixel of the YUV image 502having coordinates of (i, j) as determined above using Equation 3,(σ_(r))_(t,ij) ^(Y) is the range sigma σ_(r) for the MRF filter 504 atthe resolution level of t for the pixel of the YUV image 502 having thecoordinates of (i, j), and m₂ is a second scaling factor that is aninteger number.

As noted above, the luminance channel 503 generally includes moretexture detail as compared to the chrominance channel 508. In attemptingto remove the noise from the luminance channel 503, the removal of thenoise may have an effect of blurring the texture information included inthe luminance channel 503. Thus, removing the noise in the luminancechannel 503 via the first MRF filter 504 involves a balance betweennoise removal and texture preservation.

The chrominance channel 508 generally includes significant chrominancenoise (i.e., color noise), which includes coarse grain noise ofrelatively large sizes. Thus, in order to eliminate the coarse grainnoise in the chrominance channel 508, a maximum value from (σ_(n))_(t)is used in determining the range sigma σ_(r) values for the second MRFfilter 510. In an example, the range sigma σ_(r) for processing thechrominance channel 508 in the second MRF filter 510 is determinedaccording to:

$\begin{matrix}{{\left( \sigma_{r} \right)_{t}^{UV} = {m_{3} \times {\max\;\left\lbrack \left( \sigma_{n} \right)_{t,{ij}} \right\rbrack}}},} & \left( {{Eqn}.\mspace{14mu} 5} \right)\end{matrix}$where max[(σ_(n))_(t,ij)] is a maximum value of (σ_(n))_(t,ij) for thedifferent resolution levels and for all pixels of the YUV image 502,(σ_(r))_(t) ^(UV) is the range sigma σ_(r) for the MRF filter 510 at theresolution level of t, and m₃ is a third scaling factor that is aninteger number.

For the wavelet threshold filtering employed in the first and second MRFfilters 504, 510, threshold values for the HL, LH, and HH frequencysub-bands are derived from (σ_(n))_(t) at each of the differentresolution levels t. Because the HL, LH, and HH frequency sub-bandsgenerally include detail information including edge and textureinformation, threshold values for the luminance and chrominance channels503, 508 are determined on a pixel-by-pixel basis, such that each pixelhas its own threshold value. In an example, the threshold values aredetermined according to:

$\begin{matrix}{{{th}_{t} = \frac{\left( \sigma_{n} \right)_{t}^{2}}{\left( \sigma_{X} \right)_{t,{ij}}}},{{{{where}\mspace{14mu}\left( \sigma_{X} \right)_{t,{ij}}} = \sqrt{\max\left( {{\left( \sigma_{Y} \right)_{t,{ij}}^{2} - \left( \sigma_{n} \right)_{t,{ij}}^{2}},0} \right)}};{\left( \sigma_{Y} \right)_{t,{ij}}^{2} = {{mean}\mspace{11mu}{\left( Y_{t,{ij}}^{2} \right).}}}}} & \left( {{Eqn}.\mspace{14mu} 6} \right)\end{matrix}$

FIG. 6 is a block diagram 600 for processing a YUV image 602 including ablock-matching three-dimensional (BM3D) filter 604 and amulti-resolution (MRF) filter 610. The MRF filter 610 performsdecomposition and noise filtering on a chrominance channel 608 of theYUV image 602. By contrast the BM3D filter 604 performs noise filteringon a luminance channel 603 of the YUV image 602 without performingdecomposition on the luminance channel 603. As illustrated in FIG. 6,the YUV image 602 includes the luminance channel 603 and the chrominancechannel 608 that are processed independently at the separate filters604, 610. In an example, the BM3D filter 604 groups matching 2D blocksof the luminance channel 603 to form 3D arrays and then appliescollaborative filtering to the 3D arrays. This method is configured toremove fine grain noise and enhance texture details of the luminancechannel 603. Based on the independent processing of the luminance andchrominance channels 603, 608, the BM3D filter 604 and the MRF filter610 output a filtered Y_(f) component 606 and a filtered U_(f)V_(f)component 612, respectively.

The BM3D filter 604 and the MRF filter 610 utilize filter parametersthat are based on noise characteristics of the YUV image 602, includingstandard deviation of noise values for the YUV image 602. In an example,the standard deviation of noise values are determined as described abovewith reference to FIG. 5. Further, parameter settings for the MRF filter610 are determined as described above with reference to FIG. 5. Todetermine parameter settings for the BM3D filter 604, the BM3D filter604 uses an estimation of the noise standard deviation σ_(n) to processthe YUV image 602 without decomposition. Specifically, in determiningthe noise estimation for the BM3D filter 604, the noise estimation isobtained from (σ_(n))₁ (i.e., (σ_(n))_(t) with t=1). The BM3D filter 604uses the noise estimation from (σ_(n))₁ because the BM3D filter 604processes the luminance channel 603 at the original resolution level ofthe luminance channel 603 without decomposition. In an example, a scaledversion of a median value from (σ_(n))₁ is used to determine the noisestandard deviation σ_(n) for the BM3D filter 604. In the example, thenoise standard deviation σ_(n) for the BM3D filter 604 is calculatedaccording to:

$\begin{matrix}{{\left( \sigma_{n} \right)^{{BM}\; 3D} = {m_{1} \times {{Median}\;\left\lbrack \left( \sigma_{n} \right)_{1,{ij}} \right\rbrack}}},} & \left( {{Eqn}.\mspace{14mu} 7} \right)\end{matrix}$where (σ_(n))^(BM3D) is the noise standard deviation σ_(n) for the BM3Dfilter 604, m₁ is a scaling factor that is an integer, andMedian[(σ_(n))_(1,ij)] is a median value of (σ_(n))_(t,ij) for allpixels of the image at the resolution level of t=1. The t=1 resolutionlevel represents the resolution level of the image 602 without anydecomposition.

The examples of FIGS. 5 and 6 utilize a pixel-based calculation ofσ_(n), such that the noise standard deviation σ_(n) is determined foreach pixel of the YUV images 502, 602. In contrast to the pixel-basedcalculations described above, in other examples, a block-basedcalculation of σ_(n) is performed. In a situation where an image iscaptured under a high light condition, chrominance noise is less seriousas compared to situations where the image is captured under a low lightcondition. In an example, when the image is captured under the highlight condition, the HH_(t) frequency sub-band is divided into N×Nblocks, where N represents a number of pixels. The calculationprocedures for determining (σ_(n))_(t,ij), (σ_(r))_(t,ij) ^(Y),(σ_(r))_(t) ^(UV), th_(t), (σ_(n))^(BM3D) according to Equations 3-7 aresimilar to those described above, except that individual pixels are notconsidered, and instead, values are calculated for each of the N×Nblocks. The use of the block-based calculation of σ_(n) reflects thefact that under the high light condition, the image should have anadequate signal-to-noise ratio at the outset, thus permitting the lesscomputationally intensive block-based calculation. In an example, whereMRF filters are used to filter and decompose an input image, eachsub-band is divided into N×N blocks for the image, where N=4.

In an example, a determination is made as to whether an input image wasacquired under a high light condition or a low light condition. If theimage was acquired under a high light condition, standard deviation ofnoise values are calculated on a block basis using the N×N blocksdescribed above. By contrast, if the image was acquired under a lowlight condition, the standard deviation of noise values are calculatedon a per-pixel basis.

In an example, the determination as to whether the input image wasacquired under a high light condition or a low light condition is usedin setting parameters of the noise filters. For example, when an MRFfilter is used to decompose the input image, if the input image wasacquired under a high light condition, the input image may be subject toa smaller number of decompositions as compared to a situation where theinput image was acquired under a low light condition. In an example, infiltering the chrominance channel using the MRF filter, two levels ofdecomposition are used if the image was acquired under the high lightcondition, and three levels of decomposition are used if the image wasacquired under the low light condition. In the example, if the luminancechannel is subject to decomposition via an MRF filter, a single level ofdecomposition may be used in filtering the luminance channel regardlessof the light condition. The use of the single level of decomposition forfiltering the luminance channel, as compared to the multiple levels ofdecomposition used for filtering the chrominance channel, reflects thefact that the luminance channel generally includes less color noise thanthe chrominance channel and thus requires less extensive noise removal.

In another example, if the input image was acquired under a high lightcondition, lower values for range sigma (σ_(r)) are used in a bilateralfilter, as compared to situations where the input image was acquiredunder a low light condition. As explained above, the range sigma valuecorresponds, generally, to a strength of the bilateral filter. In thehigh light condition, a lower strength of the bilateral filter may besufficient, and the lower strength may avoid issues caused by the use ofa higher filter strength (e.g., loss of edge and texture information inthe input image). Similarly, if the input image was acquired under ahigh light condition, lower values for the m₁, m₂, and m₃ scalingfactors are used in the BM3D and MRF filters, as compared to situationswhere the input image was acquired under a low light condition. The useof the lower values for the m₁, m₂, and m₃ scaling factors may decreasethe strength of the BM3D and bilateral filters and thus avoid the issuescaused by the use of a higher filter strength. In each of the examplesdescribed above, light sources are considered in determining parametersettings for the noise reduction filters in order to balance denoisingperformance and computational efficiency.

FIG. 7 is a flowchart 700 illustrating an example method for reducingnoise in an image. At 702, noise is reduced in a luminance channel ofthe image using a first filtering procedure. At 704, noise is reduced ina chrominance channel of the image using a second filtering procedure.At 706, the chrominance channel is decomposed into a first plurality offrequency sub-bands after the second filtering procedure, where eachfrequency sub-band of the first plurality of frequency sub-bandsrepresents the chrominance channel at a first resolution. At 708, thenoise is further reduced in the chrominance channel using a thirdfiltering procedure after the decomposing of the chrominance channelinto the first plurality of frequency sub-bands. The third filteringprocedure is applied to each frequency sub-band of the first pluralityof frequency sub-bands. At 710, a lowest frequency sub-band of the firstplurality of frequency sub-bands is decomposed into a second pluralityof frequency sub-bands after the third filtering procedure. Eachfrequency sub-band of the second plurality of frequency sub-bandsrepresents the chrominance channel at a second resolution that is lowerthan the first resolution. At 712, the noise is further reduced in thechrominance channel using a fourth filtering procedure. The fourthfiltering procedure is applied to each frequency sub-band of the secondplurality of frequency sub-bands.

This written description uses examples to disclose the invention,including the best mode, and also to enable a person skilled in the artto make and use the invention. The patentable scope of the invention mayinclude other examples. Additionally, the methods and systems describedherein may be implemented on many different types of processing devicesby program code comprising program instructions that are executable bythe device processing subsystem. The software program instructions mayinclude source code, object code, machine code, or any other stored datathat is operable to cause a processing system to perform the methods andoperations described herein. Other implementations may also be used,however, such as firmware or even appropriately designed hardwareconfigured to carry out the methods and systems described herein.

The systems' and methods' data (e.g., associations, mappings, datainput, data output, intermediate data results, final data results, etc.)may be stored and implemented in one or more different types ofcomputer-implemented data stores, such as different types of storagedevices and programming constructs (e.g., RAM, ROM, Flash memory, flatfiles, databases, programming data structures, programming variables,IF-THEN (or similar type) statement constructs, etc.). It is noted thatdata structures describe formats for use in organizing and storing datain databases, programs, memory, or other computer-readable media for useby a computer program.

The computer components, software modules, functions, data stores anddata structures described herein may be connected directly or indirectlyto each other in order to allow the flow of data needed for theiroperations. It is also noted that a module or processor includes but isnot limited to a unit of code that performs a software operation, andcan be implemented for example as a subroutine unit of code, or as asoftware function unit of code, or as an object (as in anobject-oriented paradigm), or as an applet, or in a computer scriptlanguage, or as another type of computer code. The software componentsand/or functionality may be located on a single computer or distributedacross multiple computers depending upon the situation at hand.

It should be understood that as used in the description herein andthroughout the claims that follow, the meaning of “a,” “an,” and “the”includes plural reference unless the context clearly dictates otherwise.Also, as used in the description herein and throughout the claims thatfollow, the meaning of “in” includes “in” and “on” unless the contextclearly dictates otherwise. Further, as used in the description hereinand throughout the claims that follow, the meaning of “each” does notrequire “each and every” unless the context clearly dictates otherwise.Finally, as used in the description herein and throughout the claimsthat follow, the meanings of “and” and “or” include both the conjunctiveand disjunctive and may be used interchangeably unless the contextexpressly dictates otherwise; the phrase “exclusive of” may be used toindicate situations where only the disjunctive meaning may apply.

It is claimed:
 1. A method of reducing noise in an image, the methodcomprising: reducing noise in a luminance channel of the image using afirst filtering procedure; reducing noise in a chrominance channel ofthe image using a second filtering procedure; decomposing thechrominance channel into a first plurality of frequency sub-bands afterthe second filtering procedure, each frequency sub-band of the firstplurality of frequency sub-bands representing the chrominance channel ata first resolution; further reducing the noise in the chrominancechannel using a third filtering procedure after the decomposing of thechrominance channel into the first plurality of frequency sub-bands, thethird filtering procedure being applied to each frequency sub-band ofthe first plurality of frequency sub-bands; and decomposing a lowestfrequency sub-band of the first plurality of frequency sub-bands into asecond plurality of frequency sub-bands after the third filteringprocedure, each frequency sub-band of the second plurality of frequencysub-bands representing the chrominance channel at a second resolutionthat is lower than the first resolution.
 2. The method of claim 1,wherein the first filtering procedure is configured to preserve edgeinformation or texture information in the luminance channel based onfirst filter parameters, and wherein the second filtering procedureprocesses the chrominance channel independently of a processing of theluminance channel in the first filtering procedure, the second filteringprocedure utilizing second filter parameters that are different from thefirst filter parameters.
 3. The method of claim 1, wherein the reducingof the noise in the chrominance channel using the second and thirdfiltering procedures includes: performing, using a bilateral filter,spatial averaging over a window of pixels of the chrominance channel,wherein the bilateral filter is applied to i) the chrominance channel ofthe image prior to the decomposing of the chrominance channel into thefirst plurality of frequency sub-bands, and ii) the lowest frequencysub-band of the first plurality of frequency sub-bands.
 4. The method ofclaim 1, further comprising: decomposing the luminance channel into athird plurality of frequency sub-bands after the first filteringprocedure; and further reducing the noise in the luminance channel usinga fourth filtering procedure after the decomposing of the luminancechannel into the third plurality of frequency sub-bands, wherein theluminance channel is not further decomposed following the decomposing ofthe luminance channel into the third plurality of frequency sub-bands.5. The method of claim 1, further comprising: determining standarddeviation of noise values for the image, wherein the standard deviationof noise values are determined for each unit of the image, a unitcomprising a single pixel or a group of pixels of the image; anddetermining whether the image was acquired under a high light conditionor a low light condition, wherein if the image was acquired under thehigh light condition, the unit for which the standard deviation of noisevalues are determined is the group of pixels of the image, and whereinif the image was acquired under the low light condition, the unit forwhich the standard deviation of noise values are determined is thesingle pixel of the image.
 6. The method of claim 1, further comprising:determining standard deviation of noise values for the image based on(σ_(n))_(t,ij) =m ₁×Median(|Y _(t,ij)|), where (σ_(n))_(t,ij) is astandard deviation of noise value for the image at a resolution level oft for a pixel of the image having coordinates of (i,j), m₁ is a scalingfactor, and Y_(t,ij) represents a window of pixels with a center at thecoordinates of (i,j) at the resolution level t.
 7. The method of claim1, wherein the first filtering procedure utilizes a block-matchingthree-dimensional filter, and wherein the utilizing of theblock-matching three-dimensional filter in the first filtering procedurereduces the noise in the luminance channel without decomposing theluminance channel.
 8. The method of claim 1, further comprising:determining whether the image was acquired under a high light conditionor a low light condition; and if the image was acquired under the lowlight condition: further reducing the noise in the chrominance channelusing a fourth filtering procedure after the decomposing of the lowestfrequency sub-band of the plurality of sub-bands, the fourth filteringprocedure being applied to each frequency sub-band of the secondplurality of frequency sub-bands, and decomposing a lowest frequencysub-band of the second plurality of frequency sub-bands into a thirdplurality of frequency sub-bands after the fourth filtering procedure,each frequency sub-band of the third plurality of frequency sub-bandsrepresenting the chrominance channel at a third resolution that is lowerthan the second resolution.
 9. The method of claim 1, furthercomprising: converting the image from an RGB format to a YUV format, theYUV format including the luminance channel and the chrominance channel.10. The method of claim 1, wherein the decomposing of the chrominancechannel into the first plurality of frequency sub-bands is performed viaa wavelet transformation to generate the first plurality of frequencysub-bands including an LL sub-band, an LH sub-band, a HL sub-band, andan HH sub-band, the LL sub-band being the lowest frequency sub-band ofthe first plurality of frequency sub-bands.
 11. A system for reducingnoise in an image, the system comprising: a first filter configured toreduce noise in a luminance channel of the image using a first filteringprocedure; a second filter configured to reduce noise in a chrominancechannel of the image using a second filtering procedure; a firsttransformation unit configured to decompose the chrominance channel intoa first plurality of frequency sub-bands after the second filteringprocedure, each frequency sub-band of the first plurality of frequencysub-bands representing the chrominance channel at a first resolution; athird filter configured to further reduce the noise in the chrominancechannel using a third filtering procedure after the decomposing of thechrominance channel into the first plurality of frequency sub-bands, thethird filtering procedure being applied to each frequency sub-band ofthe first plurality of frequency sub-bands; and a second transformationunit configured to decompose a lowest frequency sub-band of the firstplurality of frequency sub-bands into a second plurality of frequencysub-bands after the third filtering procedure, each frequency sub-bandof the second plurality of frequency sub-bands representing thechrominance channel at a second resolution that is lower than the firstresolution.
 12. The system of claim 11, wherein the first filteringprocedure is configured to preserve edge information or textureinformation in the luminance channel based on first filter parameters,and wherein the second filtering procedure processes the chrominancechannel independently of a processing of the luminance channel in thefirst filtering procedure, the second filtering procedure utilizingsecond filter parameters that are different from the first filterparameters.
 13. The system of claim 11, wherein the second filter andthe third filter each include a bilateral filter, the bilateral filterbeing configured to perform spatial averaging over a window of pixels ofthe chrominance channel, and wherein the bilateral filter is applied toi) the chrominance channel of the image prior to the decomposing of thechrominance channel into the first plurality of frequency sub-bands, andii) the lowest frequency sub-band of the first plurality of frequencysub-bands.
 14. The system of claim 11, further comprising: a thirdtransformation unit configured to decompose the luminance channel into athird plurality of frequency sub-bands after the first filteringprocedure; and a fourth filter configured to further reduce the noise inthe luminance channel using a fourth filtering procedure after thedecomposing of the luminance channel into the third plurality offrequency sub-bands, wherein the luminance channel is not furtherdecomposed following the decomposing of the luminance channel into thethird plurality of frequency sub-bands.
 15. The system of claim 11,further comprising: a noise analyzer configured to determine standarddeviation of noise values for the image, wherein the standard deviationof noise values are determined for each unit of the image, a unitcomprising a single pixel or a group of pixels of the image, wherein itis determined whether the image was acquired under a high lightcondition or a low light condition, wherein if the image was acquiredunder the high light condition, the unit for which the standarddeviation of noise values are determined is the group of pixels, andwherein if the image was acquired under the low light condition, theunit for which the standard deviation of noise values are determined isthe single pixel of the image.
 16. The system of claim 11, furthercomprising: a noise analyzer configured to determine standard deviationof noise values for the image based on(σ_(n))_(t,ij) =m ₁×Median(|Y _(t,ij)|), where (σ_(n))_(t,ij) is astandard deviation of noise value for the image at a resolution level oft for a pixel of the image having coordinates of (i,j), m₁ is a scalingfactor, and Y_(t,ij) represents a window of pixels with a center at thecoordinates of (i,j) at the resolution level t.
 17. The system of claim11, wherein the first filter is a block-matching three-dimensionalfilter configured to reduce the noise in the luminance channel withoutdecomposing the luminance channel.
 18. The system of claim 11, furthercomprising: a noise analyzer configured to determine whether the imagewas acquired under a high light condition or a low light condition,wherein if the image was acquired under the low light condition: afourth filter reduces the noise in the chrominance channel using afourth filtering procedure after the decomposing of the lowest frequencysub-band of the plurality of sub-bands, the fourth filtering procedurebeing applied to each frequency sub-band of the second plurality offrequency sub-bands, and a third transformation unit decomposes a lowestfrequency sub-band of the second plurality of frequency sub-bands into athird plurality of frequency sub-bands after the fourth filteringprocedure, each frequency sub-band of the third plurality of frequencysub-bands representing the chrominance channel at a third resolutionthat is lower than the second resolution.
 19. The system of claim 11,further comprising: an image conversion unit configured to convert theimage from an RGB format to a YUV format, the YUV format including theluminance channel and the chrominance channel.
 20. The system of claim11, wherein the decomposing of the chrominance channel into the firstplurality of frequency sub-bands is performed via a wavelettransformation to generate the first plurality of frequency sub-bandsincluding an LL sub-band, an LH sub-band, a HL sub-band, and an HHsub-band, the LL sub-band being the lowest frequency sub-band of thefirst plurality of frequency sub-bands.